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Journal Article•DOI•

Information Uncertainty and the Post–Earnings Announcement Drift in Europe

GerardXavier1•
The Royal Bank of Scotland1
21 Mar 2012-Financial Analysts Journal (CFA Institute)-Vol. 68, Iss: 2, pp 51-69
TL;DR: In this article, the authors investigated the effect of earnings announcement abnormal return and of abnormal trading volume on future returns for a large sample of European companies with both annual and interim announcements over 1997-2010, and found that the two measures of market surprise are positively related to future abnormal returns.
Abstract: Investigating the effect of earnings announcement abnormal return and of abnormal trading volume on future returns for a large sample of European companies with both annual and interim announcements over 1997–2010, the author found that the two measures of market surprise are positively related to future abnormal returns, especially when information uncertainty is high. These two effects also appear to be complementary in that each retains some incremental predictive power for future returns.

Summary (4 min read)

Jump to: [1. Introduction] – [2. Literature Review and Motivations] – [3.1 Sample Description] – [3.2 Variable Definitions] – [TABLE 1 ABOUT HERE]] – [13 [FIGURE 5 ABOUT HERE]] – [4.2 The Role of Information Uncertainty] – [4.3 The Earnings Announcement Abnormal Return & Abnormal Volume Effects:] – [4.4 Trading Strategy with Monthly Rebalancing] – [[TABLE 7 ABOUT HERE]] – [5.1 The Trading Strategy Returns and Stock Illiquidity] – [5.2 Earnings Announcement Abnormal Volume and Absolute Returns] – [[TABLE 10 ABOUT HERE]] and [6. Conclusion]

1. Introduction

  • It has recently been noted that some important non-earnings information is also released at the time of the announcement.
  • Last but not least, the authors investigate the role played by information uncertainty in explaining the pay-offs associated with the abnormal volume and the abnormal return effects.
  • Therefore, as with the abnormal return effect, the authors find that firms which surprise the market in one quarter continue to do so one year later.
  • The next section discusses the relevant literature and motivates their analysis.

2. Literature Review and Motivations

  • It is also obvious that some important non-earnings related news are released at the time of an announcement.
  • Finally, a number of recent empirical findings suggest that market participants incorrectly value non-financial information.
  • In line with earlier findings, Brandt et al (2006) report a strong postearnings announcement drift when ranking stocks on the basis of their abnormal return at the time of the earnings announcement.
  • Moreover, Zhang (2006) demonstrates that investors under-reaction to public information is even more pronounced in cases of greater information uncertainty.

3.1 Sample Description

  • The universe for their study consists of exchange listed firms belonging to the FTSE All-World Developed Europe Index from 1997 to 2010.
  • Interim report dates comprise semi-annual announcements as well as announcements for quarter one to quarter three.
  • Moreover, the authors use widely available 9 market data to compute their indicators of market surprise, so that the main constraint that they face in terms of data coverage is the availability of interim and fiscal year-end report dates.
  • The authors also note some seasonal patterns in the relative and absolute coverage of announcement dates.
  • The authors can see that the proportion of interim report dates is large since the beginning of their sample period.

3.2 Variable Definitions

  • The authors first indicator of market surprise is the abnormal return recorded over a three-day window centred on the announcement date.
  • It is the average daily share volume over a three-day window centred on the announcement date divided by the average daily share volume estimated over days -8 through -63.

TABLE 1 ABOUT HERE]

  • The market-adjusted quintile spread return is equal to 0.57% in the five days that follow the event and increases to 1.66% when considering a 60-day holding period.
  • Interestingly, the authors find that the premiums of the abnormal return and abnormal volume strategies are driven to a large extent by the outperformance of the quintile five of earnings announcement abnormal returns (i.e. those stocks with a positive surprise) and abnormal volume, respectively.
  • These findings are worth pointing out because they raise the possibility that the earnings announcement abnormal volume effect exists despite the medium-term price momentum anomaly.

13 [FIGURE 5 ABOUT HERE]

  • As with the abnormal return effect, the authors find, looking at Figure 5 and Table 3 (Panel B), that the premium earned by the abnormal volume strategy in the days following the announcement is not short lived.
  • The cumulative difference in the adjusted long-term performance of stocks that fall in respectively quintile five and quintile one of earnings announcement abnormal volume experiences an upward drift until 90 days after the event.
  • This period of underperformance is followed by a significant pick-up in the strategy returns, starting 250 days after the event.
  • Stocks that surprise the market in one quarter seem to continue to do so in the same quarter one year later.
  • Investors appear to be systematically surprised by these announcements.

4.2 The Role of Information Uncertainty

  • One of the theoretical explanations for the post-earnings announcement drift is that investors under-react to public information.
  • The authors use the specific volatility of stock returns as a proxy for information uncertainty and the dispersion of beliefs about valuations.
  • Bekaert et al (2010) show, for a large sample of developed countries, that aggregate idiosyncratic volatility is well described by a stationary, mean reverting process with occasional shifts to a higher mean, higher variance regime.
  • In Table 4 , the authors see, when considering a 20-day and a 60-day holding period, that a strategy based on earnings announcement abnormal return (Panel A) works significantly better within highly volatile stocks than within stocks with low volatility.
  • When considering a short horizon, however, no significant difference can be found.

4.3 The Earnings Announcement Abnormal Return & Abnormal Volume Effects:

  • In Table 6 , the authors intersect independent rankings based on earnings announcement abnormal return and abnormal volume, and measure, over different holding periods, the abnormal performance of stocks that fall in each intersection.
  • The evidence for the marginal impact of the abnormal volume is, however, more statistically significant when considering longer holding periods.
  • The quintile spread returns earned by this strategy in each intersection of abnormal volume are typically positive, and in several instances also statistically significant.
  • Therefore, their empirical evidence seems to confirm the idea that these two empirical patterns are the results of distinct behavioural biases.

4.4 Trading Strategy with Monthly Rebalancing

  • Given their independent information contents the authors investigate in this section the combined predictive power of the earnings announcement abnormal return and abnormal volume effects.
  • Indeed, under this specification, a score is used for the first time on average two weeks after the earnings announcement.
  • For their trading strategy to be easily implementable, it is important that it works within large-capitalisation names.
  • The authors also distinguish between periods of high and low aggregate specific risk on the basis of idiosyncratic risk rankings at the end of every months.
  • Equation ( 5) is ran using the monthly returns from the trading strategy of Figure 8 , where the top quintile of earnings surprises is bought and the bottom quintile sold.

[TABLE 7 ABOUT HERE]

  • The earnings surprise strategy generates a large raw quintile spread return of 0.70% per month.
  • The raw quintile spread return earned in the largest capitalisation subset of their universe is equal to 0.58% (0.56%).
  • Similarly, to the extent that investors are already aware of large firms, it is at first unclear how an earnings announcement could significantly increase their visibility.
  • As expected, the authors find, in Tables 7 and 8 , that the trading strategy earns a larger premium within those stocks that have higher levels of idiosyncratic risk.
  • The positive premium for the low volatility subset of the universe loses, however, its statistical significance once the authors control for the Fama-French / Carhart factors.

5.1 The Trading Strategy Returns and Stock Illiquidity

  • Chordia et al (2009) report that the post-earnings announcement drift in the US is prevalent mainly within relatively illiquid stocks.
  • To the extent that market capitalisation does not perfectly control for stock illiquidity, it could be that the significant quintile spread return earned by the earnings surprise strategy within the largest capitalisation names disappears once stock illiquidity is taken into account explicitly.
  • A stock is allocated in the high liquidity subset if its illiquidity score falls below the median value that month.
  • Controlling for illiquidity only marginally reduces the quintile spread return of the strategy within the largest capitalisation subset of the universe.
  • The raw and abnormal returns earned by the strategy continue to be both economically as well as statistically significant, hence possibly suggesting that their control for market capitalisation already captures most of the difference in firm liquidity.

5.2 Earnings Announcement Abnormal Volume and Absolute Returns

  • Several authors have documented a contemporaneous correlation between the trading volume of a stock and its absolute return.
  • Since absolute return closely proxy for stock volatility, it could be argued that the abnormal volume effect only reflects this risk premium.
  • To assess whether this is the case, the authors first run the monthly strategy of Figure 8 , using the earnings announcement abnormal volume and the absolute announcement return on the threeday announcement window.
  • Second, every months the authors run two cross-sectional regressions of the earnings announcement abnormal volume of each stock against their absolute announcement return.

[TABLE 10 ABOUT HERE]

  • Table 10 shows that the absolute return strategy fails to earn a significant quintile spread return over the period of their study.
  • In contrast, the volume strategy generates a significant raw quintile spread return of around 0.51% (0.38%) over the study 21 period.
  • Moreover, controlling for the absolute return around the announcement reduces but does not eliminate the significance of the premium earned by the earnings announcement abnormal volume strategy.

6. Conclusion

  • By studying these anomalies in a large sample of European firms that spans a period of 14 years.the authors.
  • Finally, to avoid biasing their statistical tests, the authors consider the earnings surprise of a firm only if its post-event returns do not overlap with those of its previous surprise.
  • 5 the authors look at the performance of the earnings announcement abnormal return (Panel A) and abnormal volume (Panel B) strategies following periods of high and low aggregate specific risk.
  • To compute the characteristics benchmark, the authors match each firm to a portfolio of securities that fall in the same Size (market capitalization), Book-to-Market and Momentum terciles.
  • The authors then use those rankings to create quintile spread portfolios that they hold until the end of the following month, when the ranking process is repeated.

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Information Uncertainty and the Post-Earnings Announcement Drift in Europe
Xavier Gerard
xavier.gerard@rbs.com
The Royal Bank of Scotland
250 Bishopsgate, London UK, EC2M 4AA
Abstract
This paper investigates the relation between earnings announcement abnormal return,
abnormal trading volume, and subsequent returns for a large sample of European firms. In
addition to bringing new insights for the dynamics of the abnormal return and abnormal
trading volume effects, our analysis provides out-of-sample confirmations of several prior US
findings. We show that each measure of market surprise is positively related to future
abnormal returns, and especially so when information uncertainty is high. These two effects
appear to be complementary as each retain some incremental predicting power for future
returns. Finally, our empirical evidence is not limited to small, illiquid, stocks; and it is robust
to controlling for potential market microstructure biases.
JEL classification: M4, G12, G14
Keywords: Market efficiency; Trading Volume; High-Volume Premium; Post-Earnings
Announcement Drift; Earnings Surprises; Information Uncertainty
2
1. Introduction
Since Ball and Brown (1968), over forty years ago, an extensive body of mainly US
academic research has documented a positive relation between earnings forecast errors,
computed using either a time series approach or analyst estimates, and abnormal post-
announcement returns. In contrast, due to several data limitations, the evidence for the post-
earnings announcement drift in Europe is relatively scarce. The historical coverage for analyst
forecasts of interim numbers, as well as for the actual interim numbers released by the firms,
is typically low or unreliable in Europe. Moreover, pan-European accounting studies are
plagued by differences of accounting practices across member states.
This paper extends the US evidence to a large sample of European firms with annual as
well as interim earnings announcements from 1997 to 2010. To avoid using analyst forecasts
of interim numbers or the actual interim numbers released by the firm, we resort to some
recent academic findings and quantify the degree of market surprise with some market related
information, namely, the abnormal return and the abnormal trading volume at the time of the
announcement. The use of market data implies that our metrics of market surprise can be
easily computed for a large number of European firms. Moreover, by using market data we
largely alleviate concerns related to differences of accounting practices across European
countries.
The current predominant belief for the post-earnings announcement drift is that it is
caused by some form of under-reaction to the information contained in earnings
announcements. Although previous empirical work has focused on the earnings surprise, it
has recently been noted that some important non-earnings information is also released at the
time of the announcement. For example, firms provide information about components of
earnings such as sales, and operating margins. Still further, earnings announcements tend to
be accompanied by conference calls and press releases where valuable information is
disseminated. Therefore, to the extent that the abnormal return captures a wide range of
earnings and non-earnings related news, it could be argued that it is a broader measure of
market surprise than traditional proxies.
In contrast with the post-earnings announcement drift of Ball and Brown (1968), the
abnormal volume anomaly has been discovered recently. It refers to the outperformance of
high volume stocks relative to low volume stocks following an earnings announcement.
Explanations for this anomaly includes models of capital market equilibrium (Merton, 1987),
behavioural biases arising from the limitations of individual investors to process large
3
amounts of information (Barber and Odean, 2004), and risk-based arguments where the
abnormal volume is seen as a proxy for opinion divergence (Garfinkel and Sokobin, 2006).
Given that the empirical evidence for this effect is still limited, extending the analysis to the
European market should help bring about some valuable insights.
Last but not least, we investigate the role played by information uncertainty in explaining
the pay-offs associated with the abnormal volume and the abnormal return effects. Our proxy
for information uncertainty is the idiosyncratic volatility of the securities in our sample.
Hirshleifer (2001) posits that greater uncertainty combined with the lack of accurate feedback
about fundamentals leave more room for behavioural biases. Although behavioural biases
could well be accentuated in settings of higher information uncertainty, it is also possible that
idiosyncratic risk constitutes a limit to arbitrage preventing investors from eliminating the
anomalies. In any case, we predict that high idiosyncratic risk is associated with larger drifts,
and this analysis should help further our understanding of the mechanisms at play behind
these two empirical patterns.
Our results are four-fold. First, we find, in line with the US evidence, that a measure of
market surprise, computed as the abnormal return around an earnings announcement, is
positively related with future returns in Europe. The effect is not short lived as we show that
firms with a positive surprise in one quarter continue to surprise the market, in the same
direction, up to one year after the announcement.
Second, we are able to demonstrate, to our knowledge for the first time in Europe, the
existence of a strong abnormal volume anomaly following earnings announcements. Firms
with high abnormal volume around their earnings announcement date outperform low
abnormal volume stocks for up to ninety days after the event. This outperformance is
followed by a short period of underperformance, which strongly reverts one year after the
event. Therefore, as with the abnormal return effect, we find that firms which surprise the
market in one quarter continue to do so one year later. This evidence is echoed by patterns of
abnormal trading volume following the earnings announcement date. Those firms with the
highest abnormal trading volume in one quarter also experience a surge in their trading
volume one year later. All in all, these results strongly suggest that investors in Europe fail to
understand the implications of current announcements for future ones.
Third, as predicted, we show that the anomalies tend to generate stronger premiums when
information uncertainty is larger. For instance, although significant abnormal returns are
observed within stocks that have high and low idiosyncratic risk, we find that the abnormal
4
return and abnormal volume effects are typically stronger within those stocks that experience
the highest level of idiosyncratic volatility. Similarly, we show that the abnormal volume
effect arises primarily during periods of high aggregate idiosyncratic risk.
Finally, the two effects appear to capture independent dimensions of the market surprise
around an earnings announcement. Controlling for the degree of abnormal volume or
abnormal return around an earnings announcement, we continue to find that the other effect is
associated with positive future returns. Given their independent information contents we
investigate their combined predictive power. We find that a trading strategy based on these
two indicators of market surprise earns a monthly quintile spread return of approximately
0.70%. The trading strategy is implemented with a monthly rebalancing frequency so that a
signal is used for the first time on average two weeks after the earnings announcement, hence
alleviating concerns that our findings are contaminated by potential market microstructure
biases. Controlling for risk does not explain away the premium earned by the combined
strategies. Moreover, these findings are not restricted to small firms, and they are robust to
taking into account stock illiquidity and the volatility of stock returns over the announcement
days.
The remainder of this paper is organised as follows. The next section discusses the
relevant literature and motivates our analysis. The third section describes the sample data and
introduce the main variables of this study. The fourth section presents and discusses our
empirical findings. We test the robustness of our results in section five. Finally, the last
section concludes the study.
2. Literature Review and Motivations
This analysis is related to prior US findings that show a positive association between
measures of earnings surprise and future abnormal returns. The earnings surprise indicator,
calculated at the time of an announcement, is typically defined as the scaled difference
between the actual earnings figure and a proxy for market expectations computed using either
analyst forecasts or the time series of prior earnings. Despite having been extensively
researched since its discovery more than forty years ago by Ball and Brown (1968), the
reason for the existence of the post-earnings announcement drift continues to be heavily
debated. The prior empirical evidence offers little support for a risk-based explanation or
potential flaws in research design (Bernard and Thomas, 1989). Instead, the current
predominant belief for the post-earnings announcement drift is that it is caused by some form
of under-reaction to the information contained in earnings announcements. The exact nature
5
of the under-reaction remains vague however. Bernard and Thomas (1990) point out that the
drift occurs at subsequent earnings announcement dates. This finding lead them and others
(see for instance Ball and Bartov, 1996) to argue that investors fail to understand the
implications of current earnings for future ones. However, Jacob et al (1999) offer an
alternative interpretation of the causes of the post-earnings announcement drift when looking
at the autocorrelation structure of forecast errors, and Livnat and Mendenhall (2006) put
forward an explanation based on a more general hypothesis of under-reaction to earnings
information.
Although the bulk of the prior evidence has focused on the earnings surprise, little
attention has been paid to non-earnings information. However, considering non-earnings
related news make sense for several reasons. For instance, Liu and Thomas (2000) show that
a significant portion of the market reaction around earnings announcement is due to non-
earnings related information. It is also obvious that some important non-earnings related news
are released at the time of an announcement. For example, firms provide information about
components of earnings such as sales, and operating margins (see Jegadeesh and Livnat,
2006). Still further, earnings announcements tend to be accompanied by conference calls and
press releases where some additional valuable information is disseminated. Finally, a number
of recent empirical findings suggest that market participants incorrectly value non-financial
information. For instance, Ragjopal et al (2003) find that investors overestimate the valuation
implications of order backlogs, while Gu (2005) and Deng et al (1999) show that investors
systematically underweight patent counts, as well as the level change in patent citations.
In this study we follow Brandt et al (2006) and use as our first measure of market surprise
the abnormal stock return around an earnings announcement date. To the extent that the
abnormal return captures a wide range of earnings and non-earnings related news, both
tangible and intangible, it could be argued that it is a broader measure of market surprise than
previous indicators. In line with earlier findings, Brandt et al (2006) report a strong post-
earnings announcement drift when ranking stocks on the basis of their abnormal return at the
time of the earnings announcement. However, it has to be noted that the magnitude of the
price change around the earnings announcement could also be increasing in the information
content of the news and decreasing in the degree of under-reaction.
Using market data as a proxy for the market surprise also achieves another objective, as it
helps alleviate several data issues that have plagued European studies. First, the historical
coverage for analyst forecasts of interim numbers, as well as for the actual interim numbers
released by the firms, is typically low or unreliable in Europe. As a result the empirical
Citations
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A review of the Post-Earnings-Announcement Drift

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Representative agent earnings momentum models: the impact of sequences of earnings surprises on stock market returns under the influence of the Law of Small Numbers and the Gambler's Fallacy

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Aloysius Igboekwu
01 Jan 2015
TL;DR: Barberis et al. as discussed by the authors examined the response of a representative agent investor to sequences (streaks) of quarterly earnings surprises over a period of twelve quarters using the United States S&P500 constituent companies sample frame in the years 1991 to 2006.
Abstract: This thesis examines the response of a representative agent investor to sequences (streaks) of quarterly earnings surprises over a period of twelve quarters using the United States S&P500 constituent companies sample frame in the years 1991 to 2006. This examination follows the predictive performance of the representative agent model of Rabin (2002b) [Inference by believers in the law of small numbers. The Quarterly Journal of Economics. 117(3).p.775 816] and Barberis, Shleifer, and Vishny (1998) [A model of investor sentiment. Journal of Financial Economics. 49. p.307 343] for an investor who might be under the influence of the law of small numbers, or another closely related cognitive bias known as the gambler s fallacy. Chapters 4 and 5 present two related empirical studies on this broad theme. In chapter 4, for successive sequences of annualised quarterly earnings changes over a twelve-quarter horizon of quarterly earnings increases or falls, I ask whether the models can capture the likelihood of reversion. Secondly, I ask, what is the representative investor s response to observed sequences of quarterly earnings changes for my SP that is the frequency with which small information about stock value filters into the market is one of the factors that foment earnings momentum in stocks. However, information discreteness does not subsume the impact of sequences of annualised quarterly earnings changes, or earnings streakiness as a strong candidate that drives earnings momentum in stock returns in my S&P500 constituent stock sample. Therefore, earnings streakiness and informational discreteness appear to have separate and additive effects in driving momentum in stock price. In chapter 5, the case for the informativeness of the streaks of earnings surprises is further strengthened. This is done by examining the explanatory power of streaks of earnings surprises in a shorter horizon of three days around the period when the effect of the nature of earnings news is most intense in the stock market. Even in shorter windows, investors in S&P500 companies seem to be influenced by the lengthening of negative and positive streaks of earnings surprises over the twelve quarters of quarterly earnings announcement I study here. This further supports my thesis that investors underreact to sequences of changes in their expectations about stock returns. This impact is further strengthened by high information uncertainties in streaks of positive earnings surprise. However, earnings streakiness is one discrete and separable element in the resolution of uncertainty around equity value for S&P 500 constituent companies. Most of the proxies for earnings surprise show this behaviour especially when market capitalisation, age and cash flow act as proxies of information uncertainty. The influence of the gambler s fallacy on the representative investor in the presence of information uncertainty becomes more pronounced when I examine increasing lengths of streaks of earnings surprises. The presence of post earnings announcement drift in my large capitalised S&P500 constituents sample firms confirms earnings momentum to be a pervasive phenomenon which cuts across different tiers of the stock markets including highly liquid stocks, followed by many analysts, which most large funds would hold.

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Posted Content•
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Reon Odendaal1•
European Business School London1
15 Apr 2014-Social Science Research Network
TL;DR: In this article, the authors focus on the earnings announcements and investigate financial market efficiency, post earnings announcement drift and the presence of abnormal returns during the assessed period and show a picture in time of stock price impacts when information is released to financial markets.
Abstract: The period of 2010-2012 was characterized by information uncertainty and market volatility. Information uncertainty is a critical characteristic of financial market behavior. The ability to absorb and distribute information is central to financial market efficiency. Uncertain corporate earnings information causes stock price volatility which in turn impacts stock price equilibrium levels. An event study shows a picture in time of stock price impacts when information isreleased to financial markets. This picture can give indications of the levels of financial market efficiency. This event study focusses on the earnings announcements and investigates financial market efficiency, post earnings announcement drift and the presence of abnormal returns during the assessed period. This study seeks to add to the existing literature of event studies.

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TL;DR: In this article, the authors present existing evidence supporting and contradicting post earnings announcement drift (PEAD) and various techniques used to verify the phenomenon and conclude that this strategy produces an abnormal return of between 2.6% and 9.37% per quarter, according to various authors.
Abstract: Analysing earning’s predictive power on stock returns was in the heart of academic research since late 60’s. First introduced to academic world in 1967 during seminar “Analysis of Security Prices” by Chicago University Professors Ray Ball and Philip Brown. In the next four decades was extensively analysed by many academics and is now a well-documented anomaly and is referred to as Post Earnings Announcement Drift (PEAD). This phenomenon is still at the centre of academic research because it stands at odds with efficient market hypothesis which assumes that all information is instantaneously reflected in stock prices. Professional investors are also closely looking at PEAD as it implies that it is easy to beat the market average by simply ranking stocks based on their earnings surprise and investing in the top decile, quintile or quartile and shorting the bottom part. Academic evidence shows that this strategy produces an abnormal return of somewhere between 2.6% and 9.37% per quarter, according to various authors. In this paper I will present existing evidence supporting and contradicting “PEAD”, the history of academic research in that field and various techniques used to verify the phenomenon. The paper is organised as follows: first the history of the PEAD academic research is presented, in the second more recent evidence and research techniques used by authors are presented and finally conclusions and various critics of PEAD are shown.

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References
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Posted Content•
Post-earnings-announcement drift - delayed price response or risk premium

[...]

Victor L. Bernard, Jacob K. Thomas
01 Jan 1989-Journal of Accounting Research
TL;DR: In this paper, the authors seek to discriminate between competing explanations of post-earnings-announcement drift, and find that one class of explanations suggests that at least a portion of the price response to new information is delayed.
Abstract: This study seeks to discriminate between competing explanations of "post-earnings-announcement drift." Ball and Brown [1968] were the first to note that even after earnings are announced, estimated cumulative "abnormal" returns continue to drift up for "good news" firms and down for "bad news" firms. Foster, Olsen, and Shevlin [1984] (henceforth FOS) are among the many who have replicated the phenomenon.' FOS estimate that over the 60 trading days subsequent to an earnings announcement, a long position in stocks with unexpected earnings in the highest decile, combined with a short position in stocks in the lowest decile, yields an annualized "abnormal" return of about 25%, before transactions costs. Competing explanations for post-earnings-announcement drift fall into two categories. One class of explanations suggests that at least a portion of the price response to new information is delayed. The delay

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Journal Article•DOI•
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TL;DR: In this article, the authors investigate the role of information in affecting a firm's cost of capital, and they show that differences in the composition of information between public and private information affect the costs of capital.
Abstract: We investigate the role of information in affecting a firm's cost of capital. We show that differences in the composition of information between public and private information affect the cost of capital, with investors demanding a higher return to hold stocks with greater private information. This higher return arises because informed investors are better able to shift their portfolio to incorporate new information, and uninformed investors are thus disadvantaged. In equilibrium, the quantity and quality of information affect asset prices. We show firms can influence their cost of capital by choosing features like accounting treatments, analyst coverage, and market microstructure.

2,082 citations

Journal Article•DOI•
Improved Methods for Tests of Long-Run Abnormal Stock Returns

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John D. Lyon1, Brad M. Barber1, Chih-Ling Tsai1•
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TL;DR: Barber and Lyon as mentioned in this paper analyzed tests for long-run abnormal returns and document that two approaches yield well-specified test statistics in random samples, but misspecification in non-random samples is pervasive.
Abstract: We analyze tests for long-run abnormal returns and document that two approaches yield well-specified test statistics in random samples. The first uses a traditional event study framework and buy-and-hold abnormal returns calculated using carefully constructed reference portfolios. Inference is based on either a skewnessadjusted t-statistic or the empirically generated distribution of long-run abnormal returns. The second approach is based on calculation of mean monthly abnormal returns using calendar-time portfolios and a time-series t-statistic. Though both approaches perform well in random samples, misspecification in nonrandom samples is pervasive. Thus, analysis of long-run abnormal returns is treacherous. COMMONLY USED METHODS TO TEST for long-run abnormal stock returns yield misspecified test statistics, as documented by Barber and Lyon ~1997a! and Kothari and Warner ~1997!. 1 Simulations reveal that empirical rejection levels routinely exceed theoretical rejection levels in these tests. In combination, these papers highlight three causes for this misspecification. First, the new listing or survivor bias arises because in event studies of long-run abnormal returns, sampled firms are tracked for a long post-event period, but firms that constitute the index ~or reference portfolio! typically include firms that begin trading subsequent to the event month. Second, the rebalancing bias arises because the compound returns of a reference portfolio, such as an equally weighted market index, are typically calculated assuming periodic ~generally monthly! rebalancing, whereas the returns of sample firms are compounded without rebalancing. Third, the skewness bias arises because the distribution of long-run abnormal stock returns is positively skewed, * Graduate School of Management, University of California, Davis. This paper was previously entitled “Holding Size while Improving Power in Tests of Long-Run Abnormal Stock Re

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"Information Uncertainty and the Pos..." refers methods in this paper

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Evidence that stock prices do not fully reflect the implications of current earnings for future earnings

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Victor L. Bernard1, Victor L. Bernard2, Jacob K. Thomas3•
University of Michigan1, Harvard University2, Columbia University3
01 Dec 1990-Journal of Accounting and Economics
TL;DR: This paper found that the three-day price reactions to announcements of earnings for quarters t + 1 through I + 4 are predictable, based on earnings of quarter r. Even more surprisingly, the signs and magnitudes of the three day reactions are related to the autocorrelation structure of earnings, as if stock prices fail to reflect the extent to which each firm's earnings series differs from a seasonal random walk.

2,039 citations

Journal Article•DOI•
Investor Psychology and Asset Pricing

[...]

David Hirshleifer1•
Max M. Fisher College of Business1
01 Aug 2001-Journal of Finance
TL;DR: In this paper, the authors present a framework for understanding decision biases, evaluates the a priori arguments and the capital market evidence bearing on the importance of investor psychology for security prices, and reviews recent models.
Abstract: The basic paradigm of asset pricing is in vibrant f lux. The purely rational approach is being subsumed by a broader approach based upon the psychology of investors. In this approach, security expected returns are determined by both risk and misvaluation. This survey sketches a framework for understanding decision biases, evaluates the a priori arguments and the capital market evidence bearing on the importance of investor psychology for security prices, and reviews recent models.

1,796 citations

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Frequently Asked Questions (7)
Q1. What are the contributions in "Information uncertainty and the post-earnings announcement drift in europe" ?

This paper investigates the relation between earnings announcement abnormal return, abnormal trading volume, and subsequent returns for a large sample of European firms. In addition to bringing new insights for the dynamics of the abnormal return and abnormal trading volume effects, their analysis provides out-of-sample confirmations of several prior US findings. The authors show that each measure of market surprise is positively related to future abnormal returns, and especially so when information uncertainty is high. Finally, their empirical evidence is not limited to small, illiquid, stocks ; and it is robust to controlling for potential market microstructure biases. 

the authors use widely availablemarket data to compute their indicators of market surprise, so that the main constraint that the authors face in terms of data coverage is the availability of interim and fiscal year-end report dates. 

In other words, it is in within those stocks that suffer form larger degrees of information uncertainty and/or higher limits to arbitrage that the authors find the strongest evidence of abnormal behaviour. 

Bekaert et al (2010) show, for a large sample of developed countries, that aggregate idiosyncratic volatility is well described by a stationary, mean reverting process with occasional shifts to a higher mean, higher variance regime. 

For instance, Liu and Thomas (2000) show that a significant portion of the market reaction around earnings announcement is due to nonearnings related information. 

each month the authors run:i t i tt i ttt i t Return AbsoluteReturn AbsoluteVolume Abnormal εββα +⋅+⋅+= −−++ (8)The residuals of these regressions are then used in monthly strategies, to assess whether, controlling for absolute return, the abnormal volume strategy continues to generate a significant return. 

To the extent that market capitalisation does not perfectly control for stock illiquidity, it could be that the significant quintile spread return earned by the earnings surprise strategy within the largest capitalisation names disappears once stock illiquidity is taken into account explicitly.